Household Socioeconomic Status and Traffic Pollution Exposure in Urban Los Angeles
Bibliographic record
Abstract
Motor vehicle emissions are a major contributor to urban air pollution and are associated with many health outcomes including cardiovascular diseases, pregnancy outcomes, and asthma and other respiratory problems. We assessed whether household socioeconomic status (SES) is correlated with exposure to motor vehicle emissions in Los Angeles (LA), California. We conducted a geospatial analysis using ArcMap 10.2 and publicly available data on highways, household addresses and median household income available through the LA County GIS Data Portal, the LA Times Data Desk and the U.S. Census Bureau. We mapped major highways using ArcMap 10.2. We assessed median household income for 167,713 addresses within a 38 km radius using census data. We applied a 750 ft buffer based on previous findings that children living in homes <750 ft from the highway had an increased risk of developing leukemia. We categorized homes into five quintiles of income in order to estimate the number of homes in each income quintile that fell within the 750 ft buffer. We found that, within the 750 ft buffered region along high-traffic highways in LA, almost all homes (97%) were classified into the two lowest income quintiles: <1% of homes in the two highest (4th and 5th) income quintiles fell within the buffer, 2% in the 3rd quintile, 71% in the 2nd quintile, and 27% in the lowest quintile. (Fig. 1) We found that low SES households in L.A. are more likely to be closer to highways and thus exposed to motor vehicle emissions than higher SES households.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".